Evolutionary Multitasking Local Community Detection on Attributed Networks

Local community detection (LCD) aims to find a community containing a given starting node using local information, which becomes a hot research topic in the area of community detection. Most mainstream studies in this field only consider network topology information, neglecting node attribute information. Some studies consider both, but these methods are limited by inflexible predefined structures. When the local community topology structure is unclear or the node attributes are heterogeneous, the performance of these methods decreases. In this paper, in order to better balance topological and attribute information, we propose an evolutionary multitasking local community detection (EMLCD) framework to solve the problem of local community detection on attributed networks. In EMLCD, the LCD problem is formulated as a multitasking optimization problem with two tasks, that is, Top-Task detects local community with tight topological connections, and Att-Task detects local community with homogeneous node attributes. The advantage of using evolutionary multitasking to detect local community on attributed networks is that the correlation between these tasks can be used to enhance detection performance. A novel knowledge transfer strategy is proposed to facilitate positive transfer of knowledge between the two tasks by leveraging the complementarity of the topology information and attribute information. Finally, a solution selection strategy is proposed to guide the decision maker in selecting the ideal solution from the set of solutions generated by the two tasks. Experimental results show that the proposed framework is very effective in solving the problem of local community detection on attributed networks.